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Contents

Indicator definition

This indicator shows absolute changes and rates of change in average near-surface temperature for the globe and for a region covering Europe. Near-surface air temperature gives one of the clearest and most consistent signals of global and regional climate change, especially in recent decades. It has been measured for many decades or even centuries at some locations and a dense network of stations across the globe, and especially in Europe, provide regular monitoring of temperature, using standardised measurements, quality control and homogeneity procedures.

This indicator provides guidance for the following policy-relevant questions:

Will the global average temperature increase stay within the UNFCCC policy target of 2.0°C above pre-industrial levels?

Will the rate of global average temperature increase stay below the indicative proposed target of 0.2°C increase per decade?

Global average annual temperature deviations, ‘anomalies’, are discussed relative to a ‘pre-industrial’ period between 1850 and 1899 (beginning of instrumental temperature records). During this time, anthropogenic greenhouse gases from the industrial revolution (between 1750 and 1850) are considered to have a relatively small influence on climate compared to natural influences. However it should be noted that owing to earlier changes in the climate due to internal and forced natural variability there was not one single pre-industrial climate and it is not clear that there is a rigorous scientific definition of the term ‘pre-industrial climate’.

Temperature changes also influence other aspects of the climate system which can impact on human activities, including sea level, intensity and frequency of floods and droughts, biota and food productivity and infectious diseases. In addition to the global average target, seasonal variations and spatial distributions of temperature change are important, for example to understand the risks that current climate poses to human and natural systems and to assess how these may be impacted by future climate change.

Units

Global average annual temperature is expressed here relative to a ‘pre-industrial’ baseline period of 1850 to 1899, and this period coincides with the beginning of widespread instrumental temperature records. During this time anthropogenic GHGs (greenhouse gases) from industrial activity before 1850 had a relatively small influence on climate compared to natural influences. However, it should be noted that there is no rigorous scientific definition of the term ‘pre-industrial climate’ because climate also changed prior to 1850 due to internal and forced natural variability. Other studies sometimes use a different climatological baseline period, such as the 1971-2000 period used in parts of the IPCC Working Group One contribution to the Fifth Assessment Report (IPCC, 2013).

Specific policy question: What is the trend and rate of change in the European annual and seasonal temperature?

European average air temperature anomalies (1850 to 2012) in °C over land areas only

Note:The sources of the original data: 1) Black line - HadCRUT4 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Morice et al. 2012) with the grey area representing the 95% confidence range, 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010). Upper graph shows anomalies and lower graph shows decadal average anomalies for the same datasets.
Europe is defined as the area between 35° to 70° North and -25° to 30° East, plus Turkey (35° to 40° North and 30° to 45° East).

European average air temperature anomalies (1850 to 2012) in °C over land areas only, for annual (upper), winter (middle) and summer (lower) periods

Note:European average air temperature anomalies (1850 to 2012) in °C over land areas only, for annual (upper), winter (middle) and summer (lower) periods relative to pre-industrial baseline period. 1) Black line - HadCRUT4 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Morice et al. 2012) with the grey area representing the 95% confidence range, 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010).

Note:Grid boxes outlined in solid black contain at least three stations and so are likely to be more representative of the grid box. High confidence in the long-term trend is
shown by a black dot. (In the map above, this is the case for all grid boxes.) Area averaged annual time series of percentage changes and trend lines are shown
below each map for one area in northern Europe (green line, 5.6 ° to 16.9 °E and 56.2 ° to 66.2 °N) and one in south-western Europe (purple line, 350.6 ° to 1.9 °E and 36.2 ° to 43.7 °N).

Note:Projected changes in annual near-surface air temperature (°C) using multi-model ensemble average of RCM simulations for the period 2021-2050 (left) and 2071-2100 (right). Model simulations of the EU-ENSEMBLES project using the IPCC SRES A1B emission scenario for the periods 1961-1990, 2021-2050 and 2071-2100 (van der Linden and Mitchell, 2009).

Note:How to read the map:
Warm days are defined as being above the 90th percentile of the daily maximum temperature.
Grid boxes outlined in solid black contain at least 3 stations and so are likely to be more representative of the grid-box. Higher confidence in the long-term trend is shown by a black dot. Area averaged annual time series of percentage changes and trend lines are shown below each map for one area in northern Europe (Green line, 5.6 to 16.9 E and 56.2 to 66.2 N) and one in south-western Europe (Pink line, 350.6 to 1.9 E and 36.2 to 43.7 N).

Note:How to read the map:
Cool nights are defined as being below the 10th percentile of the daily minimum temperature. Grid boxes outlined in solid black contain at least 3 stations and so are likely to be more representative of the grid-box. Higher confidence in the long-term trend is shown by a black dot. Area averaged annual time series of percentage changes and trend lines are shown below each map for one area in northern Europe (Green line, 5.6 to 16.9 E and 56.2 to 66.2 N) and one in south-western Europe (Pink line, 350.6 to 1.9 E and 36.2 to 43.7 N).

Projections of extreme temperatures as represented by the combined number of hot summer (June-August) days (TMAX>35°C) and tropical nights (TMIN>20°C)

Note:Maps show changes in extreme temperature for two future periods, relative to 1961-1990. Extreme temperatures are represented by the combined number of hot summer (June-August) days (TMAX>35°C) and tropical nights (TMIN>20°C). All projections are the average of 5 Regional Climate Model simulations of the EU-ENSEMBLES project using the IPCC SRES A1B emission scenario for the periods 1961-90, 2021-2050 and 2071-2100 (Fischer and Schär, 2010).

Specific assessment

Annual and seasonal average in Europe

Past trends

The decadal average temperature over European land areas increased by approximately 1.3°C (±0.11 °C) between pre-industrial times and the decade of 2003 to 2012 (Figure 3). The interannual temperature variability over Europe is generally much higher in winter (Figure 4 middle) than in summer. The relatively rapid warming trend since the 1980s is most clearly evident in the summer (Figure 4 lower).

Particularly large warming has been observed in the past 50 years over the Iberian Peninsula, across central and north-eastern Europe, and in mountainous regions. Over the past 30 years, warming was the strongest over Scandinavia, especially in winter, whereas the Iberian Peninsula warmed mostly in summer (Haylock et al., 2008) (Figure 5).

Projections:

Similar to the global temperature, the average temperature over Europe is projected to continue increasing throughout the 21st century. According to results from the ENSEMBLES project (van der Linden and Mitchell, 2009) the annual average land temperature over Europe is projected to increase by more than land global temperature. By the 2021-2050 period , temperature increases of between 1.0°C and 2.5°C (Figure 6 left) are noted across Europe, and by 2071-2100 this increases to between 2.5°C and 4.0°C (Figure 6 right). These results were obtained from 25 different Regional Climate Models (RCMs) performing at 25 km spatial resolution with boundary conditions from five Global Climate Models (GCMs), all using the IPCC SRES A1B emission scenario.

In addition, the average changes projected by six Regional Climate Models (RCMs) used in the EU ENSEMBLES project indicate that pronounced warming of up to 6.0°C (compared to the 1961-1990 average) is projected over southernmost Europe during summer (June to August) (Fischer and Schär, 2010). This analysis also showed that the day-to-day variability in temperature is projected the increase the most across the northern Mediterranean coastal region, as a result of changes in atmospheric circulation and the transition from wet to dry soil moisture, which has also been noted as an important mechanism influencing recent European heat waves (Fisher et al. 2007).

Temperature extremes in Europe

Past trends:

Consistent with the general warming trend observed across Europe, historic records also show that extreme high-temperatures, e.g. number of warm days and nights, and heat waves, have become more frequent, while extreme low-temperatures, e.g. cool days and nights, cold spells, and frost days, have become less frequent (Klein Tank et al. 2002; IPCC 2007a). The average length of summer heat waves over Western Europe doubled since 1880 and the frequency of hot days almost tripled (Della-Marta et al. 2007).

Since 1960, significant increases in the number of warm days (Figure 7), and decreases in the number of cool nights have been noted throughout Europe (Figure 8). Between 1960 and 2012 (December), the number of warm days across Europe increased by between 3 and 10 days per decade. Similarly, the number of cool nights decreased by between 2 and 9 days per decade. Spatially, western and central Europe have shown the largest increases in warm days/nights, and the Iberian peninsula, land areas to the south and east of the Mediterranean, north-western Europe and Scandinavia have shown the largest warming in cool days/nights.

Although the historic records show clear long-term warming trends across Europe, it is normal to observe considerable variability between and within years. In recent years for example, during 2011 average air temperature across most of Europe was well above (between 1-2°C) normal, yet during 2010 below average temperatures prevailed across much of northern, western and central Europe (WMO, 2011; WMO, 2012). Specifically, in Germany and France temperatures of 3°C and 5°C below average made it their coldest December 2010 for more than 40 years. In the UK, it was the coldest December for more than 100 years, and the second coldest in the 352-year long Central England Temperature record (WMO, 2011).

Projections:

Extreme high temperatures across Europe are projected to become more frequent and last longer during this century (IPCC 2007a, 2007b; Sillman and Roekner 2008; Haylock et al 2008; Fischer and Schär 2010). These changes are consistent with projections of future average warming, as well as observed trends over recent decades.

During the 1961 to 1990 period only a small area in southern Spain reached 50 days with both hot summer days and tropical nights. However, projections indicate (Fischer and Schär, 2010) that 50 days with these conditions would be common across most of the Mediterranean region by the 2071 to 2100 period (Figure 9).

Specific policy question: Answer to unknown question

Global average air temperature anomalies (1850 to 2012) in degrees Celsius (°C) relative to a pre-industrial baseline period

Note:Global average air temperature anomalies (1850 to 2012) in degrees Celsius (°C) relative to a pre-industrial baseline period for 3 analyses of observations: 1) Black line - HadCRUT4 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Morice et al. 2012) with the grey area representing the 95% confidence range, 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010). Upper graph shows annual anomalies and lower graph shows decadal average anomalies for the same datasets.

Rate of change of global average temperature, 1850–2012 (in ºC per decade)

Note:Rates of change of global average temperature (1850 to 2012) in ºC per decade, based on 10-year running average of the 3 datasets: 1) Black line - HadCRUT4 from the UK Met Office Hadley Centre and University of East Anglia Climate Research Unit, baseline period 1850-1899 (Morice et al. 2012), 2) Red line – MLOST from the US National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Centre, baseline period 1880-1899 (Smith et al., 2008), and 3) Blue line - GISSTemp from the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies, baseline period 1880-1899 (Hansen et al., 2010).

Specific assessment

Global assessment

Past trends

Since the end of 19th Century, records of global average temperature have shown long-term warming trends which have been especially rapid in the most recent decades. Relative to pre-industrial temperatures (taken here to be comparable with the earliest observations at the end of 19th Century), three independent analyses of global average (land and ocean) temperature using near-surface observation records: HadCRUT4 (Morice et al. 2012); NOAA-NCDC (Smith et al. 2008); and NASA-GISS (Hansen et al. 2010), show similar amounts of warming of 0.76°C, 0.78°C, and 0.81°C, respectively, by the 2003 to 2012 decade (Figure 1). The estimates differ slightly because the underlying sources differ in their methods for analysing the data and handling data gaps. This magnitude of warming corresponds to more than one third of the 2 °C warming permitted under the EU and UNFCCC 'global climate stabilization target'.

The rate at which the global average temperature has changed over the last 140 years is mostly positive, but some comparatively short periods of negative changes also occur. The positive trends occur for relatively long periods and reach peaks of around 0.2 °C per decade (Figure 2). This is at the indicative limit of the response capacity for ecosystems which is 0.2 °C per decade (WBGU, 2003; van Vliet and Leemans, 2006). In the last decade the rate of surface warming is seen to be falling, consistent with the slow down in the global average temperature rise, which is mostly due to heat transfer between upper and deep ocean waters (UK Met Office, 2013a).

Projections:

The global average temperature is projected to continue to increase throughout the 21st century, driven mainly by increases in anthropogenic greenhouse gas concentrations. Forced by a range of future possible emissions scenarios (IPCC, SRES scenarios), the warming averaged for the near future (2011-2030) compared to 1980-1999 is between +0.64°C and +0.69°C, which is consistent with that observed for the past few decades (IPCC, 2007). By the mid-century (2046-2065), projected increases of between +1.3°C and +1.8°C for the same models and scenarios were noted, and by the late 21st century (2090-2099), these ranged between +1.8°C and +4.0°C.

These scenarios assume that no additional policies to limit greenhouse gas emissions are implemented and the range results from the uncertainties in future socio-economic development and in climate models. The EU and UNFCCC Copenhagen Accord target of limiting global average warming to not more than 2.0°C above pre-industrial levels is projected to be exceeded during the second half of this century and likely around 2050, for the all six IPCC scenarios.

Justification for indicator selection

Surface air temperature gives one of the clearest signals of global and regional climate change, and it has been measured for many decades or even centuries at some locations. For this reason it has been chosen as the indicator to monitor the “ultimate target of the United Nations Framework Convention on Climate Change.

Anthropogenic influence, mainly emissions of greenhouse gases, is responsible for most of the observed increase in global average temperature in recent decades (IPCC 2013). Natural factors, such as volcano eruptions and variations in solar activity, contribute to variations in global average temperature but they cannot explain the substantial warming during the past 50 years.

The World Meteorological Organisation defines a climate normal period as 30 years. This definition reflects the substantial climate variability on shorter time scales due to natural factors (e.g. changes in system components like the El Niño Southern Oscillation, volcanic eruptions and the solar cycle). When interpreting the time series of global mean temperature change, it is important to note that the observed record shows the combination of the long-term climate change signal and substantial year to year variability. An apparent trend in the temperature record over a few consecutive years is therefore not necessarily indicative of the long term temperature trend, which requires observations over several decades.

Global average temperature changes and the rate of change are both important determinants of the magnitude of possible effects of climate change. Furthermore, trends and projections of the annual global average temperature are easy to understand and can be related to a global target.

Understanding the spatial and seasonal distribution of climate change is important for assessing the potential impacts of climate change and associated adaptation needs. For example, temperature in Europe exhibits large differences from west (maritime) to east (continental), and from south (Mediterranean) to north (Arctic).

Policy context and targets

Context description

This indicator provides guidance for the following policy-relevant questions:

Can the global average temperature increase stay below the EU and UNFCCC policy target of 2.0°C above pre-industrial levels?

Can the rate of global average temperature increase stay within the indicative proposed target of 0.2°C increase per decade?

The absolute change and rate of change in global average temperature are both important indicators of the severity of global climate change. Temperature changes also influence other components of the climate system which can impact on human activities, including the hydrosphere with oceans and the cryosphere.

Targets

To avoid serious climate change impacts, the European Council proposed in its Sixth Environmental Action Programme (6EAP), reaffirmed by the Environment Council and the European Council of 22-23 March 2005 (Presidency Conclusions, section IV (46)) and later in the Seventh Environmental Action Programme (7EAP, 2014) , that the global average temperature increase should be limited to not more than 2 0 C above pre-industrial levels. Furthermore the UNFCCC 15th conference of the parties (COP15) recognised in the Copenhagen Accord (UNFCCC, 2009) the scientific evidence for the need to keep global average temperature increase below 2 0C above pre-industrial levels. In addition, some studies have proposed a 'sustainable' target of limiting the rate of anthropogenic warming to 0.1 to 0.2 0 C per decade.

The target for absolute temperature change (i.e. 2 0C) was initially derived from the variation of global mean temperature during the Holocene, which is the period since the last ice age during which human civilization has developed. Further studies (IPCC, 2007;Vautard, 2014) have pointed out that even a global temperature change of below the 2 0C target would still result in considerable impacts. Vulnerable regions across the world, in particular in developing countries (including least developed countries, small developing island states and Africa), would be most strongly affected. The UNFCCC Copenhagen Accord (2009) therefore foresees a review in 2015 of the scientific evidence for revising the global temperature target to 1.5°C.

Mainstreaming climate change adaptation in EU policies is one of the pillars of the EU Adaptation strategy. In the Europe 2020 strategy for smart, sustainable and inclusive growth, the following is stated on combating climate change: “We must also strengthen our economies, its resilience to climate risks, and our capacity for disaster prevention and response”.

Related policy documents

Adaptation means anticipating the adverse effects of climate change and taking appropriate action to prevent or minimise the damage they can cause, or taking advantage of opportunities that may arise. It has been shown that well planned, early adaptation action saves money and lives later. This webportal provides information on all adaptation activities of the European Commission.

In April 2013 the European Commission adopted an EU strategy on adaptation to climate change which has been welcomed by the EU Member States. The strategy aims to make Europe more climate-resilient. By taking a coherent approach and providing for improved coordination, it will enhance the preparedness and capacity of all governance levels to respond to the impacts of climate change.

Methodology

Methodology for indicator calculation

Various data sets on trends in global and European temperature have been used for this indicator:

Global average seasonal and annual temperature: 3 datasets are used. The HadCRUT is collaborative products of the Met Office Hadley Centre (sea surface temperature) and the Climatic Research Unit (land temperature) at the University of East Anglia. The global mean annual temperature deviations are in the original source in relation to the base period 1961-1990. The annual deviations shown in the chart have been adjusted to be relative to the period 1880 to 1899 in order to better monitor the EU objective not to exceed 2oC above pre-industrial values.

The GISS surface temperature is a product of the Goddard Institute for Space Studies under NASA. The original source anomalies are calculated in the relation to the 1951 to 1980 base period. Annual deviations shown on the chart are adjusted to the 1880 to 1899 period to better monitor the EU objective, of a maximum 2 oC global temperature increase above the pre-industrial values. The indicator has been calculated as a combination of land and sea temperature.

The GHCN surface temperature is product of the National Climate Data Centre (NCDC) from National Oceanic and Atmospheric Administration (NOAA). Datasets are available as gridded product from 1880 onwards in monthly time step. Dataset was created from station data using the Anomaly Method, a method that uses station averages during a specified base period from which the monthly/seasonal/annual departures can be calculated. Anomalies were calculated on a monthly basis for all adjusted stations having at least 20 years of data in the 1961–1990 base period. Station anomalies were then averaged within each 5° by 5° grid box to obtain the gridded anomalies. For those grid boxes without adjusted data, anomalies were calculated from the raw station data using the same technique.

European average annual and monthly temperature: The source of the data is the latest version of the gridded CRUTEM (land only). Europe is defined as the area between 35° to 70° Northern latitude, -25° to 30° Eastern longitude, plus Turkey (35° to 40° North, 30° to 45° East). The European anomalies are in the original source in relation to the base period 1961-1990. The annual deviations shown in the chart have been adjusted to be relative to the period 1850-1899. Data source: EEA, based on CRUTEM dataset

Observed changes in warm spells and frost days indices in the period 1976 to 2009; changes in the duration of warm spells in summer (days per decade) and frequency of frost days in winter (days per decade). Warm spells are defined as a period of at least six consecutive days where the mean daily temperature exceeds the baseline temperature (average daily temperature during the 1961 to 1990 period) by 5 oC. Frost days are defined as a day with an average temperature below 0 °C. Positive values indicate an increase in frequency and negative values a decrease in frequency. Data source: http://eca.knmi.nl/ensembles

Projected changes in annual (left), summer (middle) and winter (right) near-surface air temperature (°C) in the period 2071-2100 compared to the baseline period 1971-2000 for the forcing scenarios RCP 4.5 (top) and RCP 8.5 (bottom). Model simulations are based on the multi-model ensemble average of RCM simulations from the EURO-CORDEX initiative. EURO-CORDEX is the European branch of the international CORDEX initiative, which is a program sponsored by the World Climate Research Program (WRCP) to organize an internationally coordinated framework to produce improved regional climate change projections for all land regions world-wide. The CORDEX-results served as an input for climate change impact and adaptation studies within the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).

Methodology for gap filling

Global and European average time series for monthly temperature

In the original source the long-term annual and monthly mean HadCRU global temperatures were calculated from 4349 stations for the entire period of the record. There is an irregular distribution in the time and space of available stations (i.e .denser coverage over the more populated parts of the world and increased coverage after 1950). Maps/tables giving the density of coverage through time are given for land regions by Jones (2003). The gridding method was climate anomaly method (CAM), which means the station temperature data have been converted to the anomalies according to the WMO standards (baseline period 1961-1990 and at least 15 years of station data in the period) and grid-box values have been produced by simple averaging of the individual station anomaly values within each grid box.

GISS surface temperatures were calculated using around 7200 stations from Global Historical Climatology Network, United States Historical Climatology Network (USHCN) data, and SCAR (Scientific Committee on Antarctic Research) data from Antarctic stations. Additionally satellite SST has been included for the period after 1980. Temperatures were transformed into anomalies using station normalisation based on the 1951 to 1980 baseline period. Gridding has been done with reference station method using 1200 km influence circle (Hansen et al. 2006).

Surface temperature mean anomalies from Global Historical Climatology Network-Monthly (GHCN-M) has been produced at the NCDC from 2,592 gridded data points based on a 5° by 5° grids for the entire globe. The gridded anomalies were produced from GHCN-M bias corrected data. Gridded data for every month from January 1880 to the most recent month is available. The data are temperature anomalies in degrees Celsius (Jones, 2003).

Other global climate datasets are used by the climate research community, often with a specific purpose or audience in mind, for example processed satellite Earth-observations, and climate reanalyses. Although these are not specifically constructed for climate indicator monitoring, they do show the same temperature trends described here. Recently one new global temperature dataset has been developed especially for understanding temperature trends. This is the Berkeley Earth temperature record: http://berkeleyearth.org/

Daily climate information

Although Europe has a long history in collecting climate information, datasets containing daily climate information across the continent are scarce. Furthermore, accurate climate analysis requires long term time series without artificial breaks. The objective of the ECA project was to compile such a data set, consisting of homogeneous, long-term daily climate information. To ensure a uniform analysis method and data handling, data were centrally collected from about 200 meteorological stations in most countries of Europe and parts of the Middle East. Furthermore the data were processed and analysed at one institute (i.e. KNMI) (Klok et.al. , 2008).

In order to ensure the quality of the ECA&D climate data set:

Statistical homogeneity tests have been applied to detect breaks in the time series;

the meta-information accompanied with the data has intensively been analysed, e.g. to check whether observed trends were not triggered by, for example, movements of stations;

the final data set has been compared with other data sets, like the aforementioned data set of Climatic Research Unit; and

findings of the different exercises have been discussed during workshops with representatives of countries.

Global and European average time series for monthly temperature

Grid values of HadCRUT, GISTEMP and GHCN data sets have been gridded using different interpolation techniques. Each grid-box value for the HadCRUT dataset is the mean of all available station anomaly values, except that station outliers in excess of five standard deviations are omitted (Brohan et al., 2005). GISTEMP temperature anomaly data are gridded into 8000 grid cells using reference station interpolation method with 1200 km influence circle (Hansen et al. 2006). GHCN monthly data consists of 2,592 gridded data points produced on a 5° by 5° basis for the entire globe (Jones, 2003).

Uncertainties

Methodology uncertainty

The observed increase in average air temperature, particularly during recent decades, is one of the clearest signals of global climate change.

Temperature has been measured over the centuries. There is a range of different methodologies which give similar results suggesting that uncertainty is relatively low. Three data sets have been presented here for the global temperature indicator. Global temperatures from HadCRUT, GISTEMP, and GHCN have been homogenized to minimise the effects of changing measurement methodologies and location.

Data sets uncertainty

Each observation station follows international standards for taking observations set out by WMO. Each National Meteorological Service provides reports on how its data are collected and processed to ensure consistency. This includes recording information about the local environment around the observation station and any changes to that environment. This is important for ensuring the required data accuracy and performing homogeneity tests and adjustments. There are additional uncertainties because temperatures over large areas of the Earth are not observed as a matter of routine. These elements are taken into account by factoring the uncertainty into global average temperature calculations, thereby producing a temperature range rather than one uniquely definite figure (WMO, 2013). The uncertainty of temperature data has decreased over recent decades due to wider use of agreed methodologies and denser monitoring networks. Uncertainty of the temperature data comes from sampling error, temperature bias effect and from the effect of the limited observation coverage. Annual values of global and European temperature are approximately accurate to +/- 0.05 degrees C (two standard errors) for the period since 1951. They are about four times as uncertain during the 1850s, with the accuracy improving gradually between 1860 and 1950 except for temporary deteriorations during data-sparse, wartime intervals. Estimating accuracy is difficult as the individual grid-boxes are not independent of each other and the accuracy of each grid-box time series varies through time (although the variance adjustment has reduced this influence to a large extent). The issue is discussed extensively by Jones et al. (2003), Brohan et al. (2005), and Hansen et al. (2006).

Rationale uncertainty

According to the IPCC 4th Assessment Report (IPCC, 2007), there is very high confidence that the net effect of human activities since 1750 has been one of warming. Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic GHG concentrations. Moreover, it is extremely likely that more than half of the observed increase in global average surface temperature from 1951 to 2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings together. The best estimate of the human-induced contribution to warming is similar to the observed warming over this period (IPCC, 2013).